Method for Automatic Alignment of Raster Data with Vector Data in a Geographic Information System

ABSTRACT

The present invention relates to methods for aligning raster and vector data. 
     In an embodiment, a raster/vector aligner receives raster data and an approximate vector of a feature within the raster data. The raster/vector aligner generates an edge signal by edge filtering the raster data along a direction of the approximate vector and a smoothness signal by smoothness filtering the raster data along a direction of the approximate vector. The raster/vector aligner combines the edge signal and the smoothness signal into a combined signal which is used to generate a translation vector or a signal weight for the feature within the raster data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/888,118 (Attorney Docket No. 2525.0060001), filed Sep. 22, 2010, nowallowed, which is a continuation of U.S. patent application Ser. No.11/655,292 (Attorney Docket No. 2525.0060000), filed Jan. 19, 2007, nowU.S. Pat. No. 7,869,667, both of which are incorporated herein byreference in their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to computer-aided registration of rasterdata with vector data.

2. Background Art

A geographic information system (GIS) is a system for archiving,retrieving, and manipulating data that has been stored and indexedaccording to the geographic coordinates of its elements. A classicproblem for geographic information systems is the registration ofmisaligned vector data and raster data, the vector data typicallyrepresenting road information and the raster data typically representingaerial imagery. There are a number of causes for the misalignment,including obsolete data, projection errors, inaccurate camera models,the absence of a precise terrain model, different data vendors, etc.Traditionally, the misalignment problem has been addressed by a processcalled conflation, in which a set of control point pairs is specifiedand the data is warped in a manner so as to align to the control points.With the rapid development of advanced GIS applications such as GoogleEarth, however, the amount of data is prohibitive for manual alignmentcorrection processes.

What is needed is a robust and practical method for aligning raster andvector data across varying environments.

BRIEF SUMMARY OF THE INVENTION

The present invention relates to methods for aligning raster and vectordata.

In an embodiment, a raster/vector aligner receives raster data and anapproximate vector of a feature within the raster data. Theraster/vector aligner generates an edge signal by edge filtering theraster data along a direction of the approximate vector and a smoothnesssignal by smoothness filtering the raster data along a direction of theapproximate vector. The raster/vector aligner combines the edge signaland the smoothness signal into a combined signal which is used togenerate a translation vector or a signal weight for the feature withinthe raster data.

In another embodiment, the raster/vector aligner receives raster dataand a set of approximate vectors for features within the raster data.The raster/vector aligner identifies directions of the vectors andgroups the vectors into directional chunks. The raster/vector alignerdefines search areas of the raster data for each directional chunk, eacharea encompassing at least one vector in the direction. Theraster/vector aligner generates translation information for each searcharea and corresponding vector. The raster/vector aligner generates atranslation vector and confidence ratio from the translation informationof the areas.

In another embodiment, the raster/vector aligner, receives raster dataand a set of approximate vectors for features within the raster data.The raster/vector aligner subdivides the raster data into tiles withcorresponding subsets of the approximate vectors. The raster/vectoraligner generates translation information for each the tile. Theraster/vector aligner generates global translation information for theraster data from the translation information of the tiles. Theraster/vector aligner adjusts the raster data based on the globaltranslation information to align with the set of approximate vectors.

Also, in an embodiment, a computer implemented raster/vector aligner mayoperate on a computer system.

Further embodiments, features, and advantages of the invention, as wellas the structure and operation of the various embodiments of theinvention are described in detail below with reference to accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Embodiments of the invention are described with reference to theaccompanying drawings. In the drawings, like reference numbers mayindicate identical or functionally similar elements. The drawing inwhich an element first appears is generally indicated by the left-mostdigit in the corresponding reference number.

FIG. 1 is an example image of raster data with overlaid vector datamisaligned and the strongest detected road signals by the algorithm.

FIG. 2 is an example image of raster data with overlaid vector datacorrectly aligned.

FIG. 3 is a flow chart diagram of a method for aligning raster andvector data according to an embodiment of the present invention.

FIG. 4 is a flow chart diagram of a method for aligning raster andvector data according to an embodiment of the present invention.

FIG. 5 is a flow chart diagram of a method for computing optimaltranslation and confidence ratio for a single tile according to anembodiment of the present invention.

FIG. 6 is a flow chart diagram of a method for directional road featuredetection according to an embodiment of the present invention.

FIG. 7 is a flow chart diagram of a method for computing minimum-squareoptimal translation and confidence ratio according to an embodiment ofthe present invention.

FIG. 8 is an image of raster data and filtered images of the sameaccording to an embodiment of the present invention.

FIG. 9 is an image of graphs of roughness filtered raster data accordingto an embodiment of the present invention.

FIG. 10 is an image of graphs of edge filtered raster data according toan embodiment of the present invention.

FIG. 11 is an image of a graph of the combined filtered raster data andthe corresponding raster data according to an embodiment of the presentinvention.

FIG. 12 is an image of representations of the aggregated road locationsignals according to an embodiment of the present invention.

FIG. 13 is an image of representations of the cost function according toan embodiment of the present invention.

FIG. 14 is a graph of confidence ratios according to an embodiment ofthe present invention.

FIG. 15 is a depiction of a low confidence tile according to anembodiment of the present invention.

FIG. 16 is a block diagram of a raster data and vector data aligneraccording to an embodiment of the present invention.

FIG. 17 is a diagram of an example computer system that can be used toimplement an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

While the present invention is described herein with reference toillustrative embodiments for particular applications, it should beunderstood that the invention is not limited thereto. Those skilled inthe relevant art(s) with access to the teachings provided herein willrecognize additional modifications, applications, and embodiments withinthe scope thereof and additional fields in which the invention would beof significant utility.

Overview

The present invention relates to a computer program product and methodsfor identifying features from raster data and registration of rasterdata features with vector data. Examples include, but are not limitedto, raster data having satellite images and vector data having roadlocation information.

FIG. 1 depicts an example of raster data in the form of a satelliteimage. The figure shows street map vectors in blue and detected streetsin red. As can be seen in FIG. 1, many of the street map vectors aremisaligned. FIG. 2 depicts the same satellite image with aligned roaddata in blue.

According to an embodiment, an overview of the method of aligning rasterand vector data is provided as flow diagram 300 in FIG. 3. In block 310,raster and vector data are inputted. In block 320, street vector data issorted by their directions. The algorithm to automatically align theimage and street map has two main parts. First, for each direction, roaddetection is executed at block 330. Second, minimum-square best-matchequations are solved in block 340. Finally, in block 350, a translationvector is computed. This method is depicted in greater detail in flowdiagram 400 of FIG. 4 and explained in greater detail below.

Image and Vector Preprocessing

As shown in FIG. 4, after the vector and raster data are inputted (402,404), a series of preprocessing steps are executed. In an embodiment,the raster data is scaled to a designed resolution level 17 at block406. A resolution level of 17 is preferable. However, other resolutionsmay be used. The benefits of this resolution level are described below.The vector and raster data may be overlapped, associating theapproximate vectors with their projected locations on the raster data(block 408). In block 410, the input raster data and street vector mapare subdivided into smaller rectangular tiles with common widths andheights. This step is necessary because the data to be processed usuallycovers a large area and the mismatch between the raster data and thestreet vector map is very complicated globally. FIG. 5 depicts thealgorithm for computing the optimal translation and confidence ratio foreach of these tiles. This algorithm is repeated until all tiles areprocessed (block 414). By this subdivision, the mismatch inside eachtile is relatively simple and it is possible to approximately align theimage tile with the vector tile with a rigid translation. With theconsideration of distributed processing and the resource limits of ageneral computer system, it is also practical and more plausible toprocess subdivided images instead of very large images.

However, the size of the smaller sub-pieces should be limited. First, asub-piece or a tile has to be large enough to contain a road feature init. To recognize the road segment features inside each tile, the tilesize should also be large enough to contain long street segments so thatits signal can be stronger than noise signals such as those frombuildings, slim parking lots, etc.

The resolution of the input raster data is also chosen carefully forboth precision and efficiency. This approach can be applied over a rangeof resolution, given that the linear road signal is still present.However, using too low a resolution will sacrifice the precision of theresult. Conversely, too high a resolution will make feature recognitionunnecessarily expensive because of the large amount of data to beprocessed.

In the application of this algorithm, the input resolution level ischosen to be 17, which has a resolution of 0.66 meter. Another reason tochoose this resolution, besides precision and efficiency, is that thedata at this level has the largest coverage in the database. The tilesize can be set to 4096×4096 pixels, for example. Other tile sizes havealso been tested, and the algorithm works for tile sizes as small as1024×1024 pixels.

Road Detection

Road Feature Definition

A directional road extraction method is designed based on a mathematicalmodel of road features. The physical characteristics of roads andstreets in orthogonal aerial images are summarized as follows: (1)straight and linear; (2) long and continuous; (3) often with a certainwidth; (4) often with parallel boundary; and (5) smoothness along thedirection of the road.

The presented algorithm is based on these characteristics of roadfeatures. The majority of roads and streets are straight and linear,which indicates that edge detection is helpful in road extraction. Thecontinuous feature of road implies that some form of aggregation isapplicable to increase the precision and robustness. The width and theboundary curb features are more obvious in high resolution imageries,but it still applies to low resolution as well. The road width is almostconstant within a certain segment of the road regardless of resolution,and usually the road color is different from its two sides. Thesmoothness feature is helpful to distinguish road signals from othersignals that happen to be arranged linearly such as buildings.

Besides these major characteristics of road features, the algorithm alsomakes good use of existing vector data and takes advantage of the natureof a mismatch. Following are two additional characteristics of roadfeatures. First a road feature is located within a predefined searchbuffer nearby a road vector, and second a road feature is located almostparallel to its corresponding road vector.

Road Feature Detection

In an embodiment, a road feature detection algorithm is able to quicklyand robustly detect the location of a street from a given street vectorand a search buffer within a tile. In a further embodiment, such as thatdepicted in flow diagram 500 of FIG. 5, raster data tiles and theircorresponding road vectors are inputted from the subdivision block 410of FIG. 4 (blocks 502 and 504), sorted by their direction (block 506),and grouped into directional chunks based on their direction (block 508)prior to the road feature detection algorithm. This is described ingreater detail in the Directional Aggregation section below. FIG. 6depicts the directional road detection algorithm for one of thesedirectional chunks. A single vertical street vector is used as the roaddirection chunk in the explanation. However, the method is not limitedto use of a single vector.

Directional Road Detection

In an embodiment, such as that depicted in flow diagram 600 of FIG. 6,the input road vector direction chunk (block 602) is first filtered by aminimum street segment length (block 606). The next step for directionalroad detection is to create a rectangular search buffer around eachstreet vector (block 608) on the corresponding image tile (block 604),where each street vector has a certain direction and length. Therectangular search buffer is of the same length as the street vectoraround which it was created. The width of the buffer is determined by apredefined maximum mismatch amount. The algorithm will work on thesub-image defined by the buffer. An example of a sub-image defined by arectangular buffer is depicted in image 801 of FIG. 8. Next, thesub-image is filtered by a roughness filter along the street vector(block 610). A roughness filter is like an edge filter on theperpendicular direction of the street vector. An example of a sub-imagefiltered by a roughness filter is depicted in image 802 of FIG. 8. Thesub-image is also filtered by an edge filter along the street vectordirection (block 618). An example of a sub-image filtered by an edgefilter is image 803 of FIG. 8.

Following the roughness filtering, a smoothness signal is generated.This is done by projecting the roughness filter result along the streetvector direction (block 612), and applying an inverse transform to makethe smoothness signal (block 614). The roughness filter result isdepicted in plot 901 of FIG. 9. The result after an inverse transform isapplied is depicted in plot 902 of FIG. 9. An edge signal is alsoprojected. The edge signal is projected by projecting the edge filterresult along the street vector direction to generate an edge signal(block 620). In an embodiment, the above is repeated for all vectors inthe directional chunk (block 624) and the roughness filter and edgefilter projected results are aggregated across all vectors in thedirection chunk (blocks 616 and 622). Next a coarse street signal isgenerated by convolving the edge signal and the smoothness signal (block626). The edge filter result is depicted in plot 1001 of FIG. 10. Theresult after generating the coarse street signal is depicted in plot1002 of FIG. 10. Noise created by the boundary of a parking lot in theprojected edge signal is filtered by the smoothness signal and itsstrength is reduced in the coarse street signal. Finally, the streetsignal is generated by filtering the coarse street signal using aminimum street width filter (block 628). The filtered coarse streetsignal is depicted in plot 1101 of FIG. 11. The filtered coarse streetsignal is overlaid on the sub-image in image 1102 of FIG. 11 fordemonstration. The peak position of the street signal is the mostprobable location of the detected street. In an embodiment of theinvention, multiple peak locations have been selected for each directionto increase the robustness.

Directional Aggregation

Referring back to FIG. 5, the directional road detection algorithm isapplied to all the road segments defined in the vector data (block 512).For a given road vector, the detection result is not very reliable. Theimagery or the vector data may be outdated and there may be no roadfeature to be found to match, or conversely, there may be multiplestreets near a certain vector and the correct one may not have thestrongest signal. To overcome this problem, the road vectors are sortedby their directions and grouped into discrete direction chunks (blocks506 and 508). For each direction chunk, the projected street signals areall aggregated together before “peak selection” (blocks 616 and 622 ofFIG. 6). The aggregated signal is more reliable than each individualsignal, hence the peak selection result is more robust.

An optional parameter in aggregation is the estimated width of the road,which is generated from the road type information available in the roadvector database. This parameter can be used to give major roads, such asinter-state highways, a priority in matching over local streets.

FIG. 12 shows the aggregated road location signals of all the directionsfor the example in FIG. 1 with a constant road width for resolutionlevel 17 imagery. A 3D representation of the aggregated road locationsignals is depicted in plot 1201 of FIG. 12, and a 2D projection of thesame is depicted in plot 1202 of FIG. 12.

Minimum-Square Best-Match Translation

FIG. 7 depicts the minimum-square matching approach to detect theoptimal translation vector for each tile. This approach is applied afterthe possible street locations in all the directions are detected. Theresults of directional feature detection for each tile and thecorresponding vectors (blocks 702 and 704) are inputted from the roadfeature detection (flow diagram 600 of FIG. 6).

A target cost function is defined first to model the mismatch betweendetected roads and input road vectors. For a certain tile, suppose itscorresponding road vectors have I different directions and the searchbuffer size is r_(max) on both sides of each road vector. For roadvectors in the i^(th) direction, θ_(i) is the angle of the perpendicularvector of these roads with the positive x-axis. The angles are thennormalized so that θ_(i)ε(−π/2,π/2]. In the aggregated directional roadsignals, assume J_(i) possible road locations have been detected forangle θ_(i). Let (r, α) represent a translation vector with lengthrε[−r_(max),r_(max)] and direction αε(−π/2,π/2], then the cost functioncan be expressed as:

$\begin{matrix}{{{{cost}\left( {r,\alpha} \right)} = {\sum\limits_{i = 1}^{I}{\sum\limits_{j = 1}^{J_{i}}{W_{i,j}\left( {L_{i,j} - {{rcos}\left( {\alpha - \theta_{i}} \right)}} \right)}^{2}}}}\;} & (1)\end{matrix}$

where L_(i,j) is the j^(th) road signal location for direction θ_(i),and W_(i,j) is the weight of the j^(th) road signal location fordirection θ_(i). Matrix L is computed from the results of thedirectional feature detection at block 708 of FIG. 7. So the costfunction is actually a weighted square sum of the distance betweendetected locations of road vectors and the translated road vector usinga certain translation vector (r, α).

Let

${{\frac{\partial\;}{\partial r}\mspace{14mu} ({cost})} = {{0\mspace{14mu} {and}\mspace{14mu} \frac{\partial\;}{\partial\alpha}\mspace{14mu} ({cost})} = 0}},$

solve the equations and a possible global minimum cost value can befound for a certain (r, α), while still checking for boundaryconditions.

To better understand this cost function, consider an ideal situationwhere all road vectors are either vertical (i=1) or horizontal (i=2),and the road vectors need to shift right 20 pixels (Δx=+20, Δy=0) tomake a perfect alignment. For now, assume all the weights are 1.0(Wi,j≡1.0) and only the strongest signal is picked for a direction(J₁≡1). Since I=1, θ₁=0 and θ₂=π/2, equation (1) becomes:

cost(r,α)=(L _(1,1) −r cos(α))²+(L _(2,1) −r sin(α))²

Suppose the presented road detection algorithm also works precisely asexpected for this case, then L_(1,1) should be 20 and L_(2,1)=0. So theoptimal value of this cost function can be found at r=20 and α=0.

FIG. 13 depicts two 3D representations for function cost(r, α) generatedfor the example in FIG. 1. A 3D mesh is depicted in 1301, and, to bettervisualize the minimum value location of the function, the inverse value3D mesh is depicted in 1302.

Road Signal Weight Computation

The weight for each possible location of a road vector is proportionalto the detected road signal strength at that location. It is alsoproportional to the length of the road vector, because the longer thevector is, the higher possibility the algorithm has to detect the roadcorrectly from locally similar noises such as those from buildings,parking lots, etc.

Since the filters used in directional road detections are specificallydesigned for road features, it is reasonable to expect that the detectedroad signal will have an extraordinarily strong response at the reallocation of the road in the image, while the majority of signalresponses are more like random noises. So another parameter is used incomputing the weight, that is, how distinguished the signal response isat the location compared to the average response. For the presentedalgorithm, the average and the standard deviation of the signalresponses for a direction are computed for this purpose.

Following is the formula to compute W_(i,j) for direction i location j(block 710):

$\begin{matrix}{W_{i,j} = {R_{i,j}{\sum\limits_{s = 1}^{S_{i}}{{{length}_{i,s}\left( {R_{i,j} - {{average}\left( R_{i} \right)}} \right)}/\left( {2\; {{std}\left( R_{i} \right)}} \right.}}}} & (2)\end{matrix}$

where R_(i,j) is the detected road signal for direction i at location j,and length_(i,s) is the length of the s^(th) vector of the total S_(i)vectors in direction chunk i. Values average(R_(i)) and std(R_(i)) arethe average and standard deviation values for all road signals indirection i.

Solve for Global Optimal Translation

A possible global optimal translation can be found by solving thefollowing equations:

$\begin{matrix}\left\{ {\begin{matrix}{{\frac{\partial}{\partial r}({cost})} = {{\sum\limits_{i}{\sum\limits_{j}{{W_{i,j}\left( {L_{i,j} - {r\; {\cos \left( {\alpha - \theta_{i}} \right)}}} \right)}\left( {- {\cos \left( {\alpha - \theta_{i}} \right)}} \right)}}} = 0}} \\{{\frac{\partial}{\partial\alpha}({cost})} = {{r{\sum\limits_{i}{\sum\limits_{j}{{W_{i,j}\left( {L_{i,j} - {r\; {\cos \left( {\alpha - \theta_{i}} \right)}}} \right)}{\sin \left( {\alpha - \theta_{i}} \right)}}}}} = 0}}\end{matrix}\quad} \right. & (3)\end{matrix}$

From equations (3), it is easy to get the following when r≠0:

$\begin{matrix}\left\{ {\begin{matrix}{{{e\; \sin \; \alpha} - {d\; {\cos (\alpha)}}} = {r\left( {{c\left( {{\sin^{2}(\alpha)} - {\cos^{2}(\alpha)}} \right)} + {\left( {b - a} \right){\sin (\alpha)}{\cos (\alpha)}}} \right)}} \\{{{e\; \cos \; \alpha} + {d\; {\sin (\alpha)}}} = {r\left( {{a\; {\sin^{2}(\alpha)}} - {b\; {\cos^{2}(\alpha)}} + {2\; c\; {\sin (\alpha)}{\cos (\alpha)}}} \right)}}\end{matrix}\quad} \right. & (4) \\{\mspace{79mu} {where}} & \; \\{\mspace{79mu} {a = {{\sum\limits_{i,j}{W_{i,j}{\sin^{2}\left( \theta_{i} \right)}\mspace{95mu} b}} = {\sum\limits_{i,j}{W_{i,j}{\cos^{2}\left( \theta_{i} \right)}}}}}} & (5) \\{\mspace{79mu} {c = {\sum\limits_{i,j}{W_{i,j}{\sin \left( \theta_{i} \right)}{\cos \left( \theta_{i} \right)}}}}} & \; \\{\mspace{79mu} {d = {{\sum\limits_{i,j}{W_{i,j}L_{i,j}{\sin \left( \theta_{i} \right)}\mspace{65mu} e}} = {\sum\limits_{i,j}{W_{i,j}L_{i,j}{\cos \left( \theta_{i} \right)}}}}}} & \;\end{matrix}$

Parameters a, b, c, d, and e, are computed at block 712. When equations(4) are solved, and when ae≠cd, the stationary point for an extremum ofcost can be found as

$\begin{matrix}\left\{ \begin{matrix}{\alpha_{0} = {\arctan \; \left( \frac{{bd} - {ce}}{{ae} - {cd}} \right)}} \\{r_{0} = {\frac{1}{\cos (\alpha)}\frac{{d\; \tan (\alpha)} + e}{{a\; {\tan^{2}(\alpha)}} + {2\; c\; {\tan (\alpha)}} + b}}}\end{matrix} \right. & (6)\end{matrix}$

Given the computed translation vector (r₀, α₀) (block 714), thecorresponding translation (block 716) for x and y is

$\left\{ {\begin{matrix}{{\Delta \; x} = {r_{0}{\cos \left( \alpha_{0} \right)}}} \\{{\Delta \; y} = {r_{0}\sin \; \left( \alpha_{0} \right)}}\end{matrix}\quad} \right.$

Note that theoretically the solved (r₀, α₀) is only one of severalpossible extrema for cost(r,α), and if it is not a minimum, then thereal optimal minimal cost may be found on the boundary values of α andr. However, in practical application, the solution (6) is usually howthe target cost function gets its global optional minimum.

Special Cases

There are a few special cases that have been ignored in finding thesolution to equation (3). So the costs for these special cases arecomputed separately.

Using the parameters in equations (5), the original cost(r,α) functioncan be rewritten as the following to make it easy to compute.

cost(r,α)=r ²(a sin²(α)+b cos²(α)+2c sin(α)cos(α))−2r(e cos(α)+dsin(α))+C₀  (7)

where C₀=Σ_(i,j)W_(i,j)L_(i,j)=cost(α,0), computed at block 712, is theoriginal cost of the mismatch without any translation.

Following is a list of special cases and degenerated cases that need tobe treated with extra care:

(1) r=0: for any α, cost(r,α)|_(r=0)=Σ_(i,j)W_(i,j)L_(i,j)=C₀ iscomputed and compared with cost(r₀,α₀) to verify that cost(r₀,α₀) is theglobal minimum cost value.

(2) r≠0 and cos(α)=0: cost(π/2,r)=ar²−2dr+C₀ is computed and comparedwith cost(r₀,α₀).

(3) r≠0, cos(α)≠0, ae=cd and ce≠bd: for this case, no extremum exists.

(4) ae=cd and ce=bd: for this case, α can be any angle and there willstill exist a corresponding r to make a minimum value for cost(a,r).This is a degenerated case and will happen when there is only onedirection for all the roads in the tile, which rarely occurs in realdata. For such degenerated cases, it is trivial to find the optimaltranslation direction, which should be perpendicular to the onlydirection of the roads, and the translation amount r will be the minimumof all the extrema.

Boundary Condition

The solution presented in equation (6) is one of a few stationary pointsof function cost(r, α) and it is necessary to verify it is the optimalglobal minimum.

The cost function will have its minimum at the computed stationary pointwhen its second derivatives satisfy the requirements in (8) (below) atthe point. For this case, boundary condition does not need to beconsidered and the global optimal value is either at the computedstationary point or at one of the special cases analyzed in the SpecialCases section above.

$\begin{matrix}\left\{ \begin{matrix}{{\left( {\frac{\partial^{2}}{{\partial r}\; {\partial\alpha}}({cost})} \right)^{2} - {\frac{\partial^{2}}{\partial r^{2}}({cost})\frac{\partial^{2}}{\partial\alpha^{2}}({cost})}} < 0} \\{{\frac{\partial^{2}}{\partial\alpha^{2}}({cost})} > {0\mspace{14mu} {or}\mspace{14mu} \frac{\partial^{2}}{\partial r^{2}}({cost})} > 0}\end{matrix} \right. & (8)\end{matrix}$

Otherwise, the function cost(r, α) may have a local maximum at (r₀,α₀),or does not have an extreme value at the computed stationary point, andit is necessary to check the boundary conditions of function cost(α,r)at block 718.

Since cost(α,r) is a periodic function for α, only the boundarycondition for r needs to be checked. For both r=r_(max) and r=−r_(max),dense samples are made for a in the range (−π/2, π/2] and correspondingcost(r, α) values are computed. These values are compared against thevalue at (r₀,α₀) to make sure the global minimum of cost value is alwaysfound correctly.

Although the boundary sampling approach does not look efficient, it haslittle impact on the overall performance of the algorithm because usingreal data it is very unlikely the algorithm ends up computing boundarysamples. In all the several hundreds of experiments using real GIS data,the requirements in (8) are always met so boundary sampling does notcontribute to the computational cost.

Confidence Ratio

As many other computer vision algorithms, there will always be specialcases where the presented automatic road detection algorithm does notwork. The algorithm would be more practically useful if it could reportwhether its results are reliable or not. So one of the design goals ofthe algorithm is to provide a confidence ratio together with therecommended translation vector to provide some quantitative measurementof the result.

The confidence ratio generated from the algorithm, at block 722, iscomposed of the weighted angle span factor ψ, computed at block 706, andthe normalized distance factor ω, computed at block 720, as follows:

${confidence} = \frac{\psi}{\omega}$

Normalized Distance Factor

The normalized distance factor ω is derived from the computed final costvalue of the target function cost(r, α). It can be efficiently computedusing the already computed parameters in equations (5) (block 720).

$\begin{matrix}{\omega = \sqrt{\frac{{cost}\left( {r_{0},\alpha_{0}} \right)}{\sum\limits_{i,j}W_{i,j}}}} & (9)\end{matrix}$

The normalized distance factor ω describes the average distance betweenthe detected roads and the input road vector after applying the computedtranslation. For an ideal situation where the detected roads in alldirections perfectly align with the input vector after translation, ωwill be zero or infinitely small. If after the computed translation hasbeen applied and some or all of the detected roads still have an offsetdistance from the target vectors, ω will be of a relatively large valueand indicate that something might be wrong.

Weighted Angle Span

The weighted angle span factor is designed to capture the quality of theinput vector data. Let V_(i) be an aggregated road vector with directionθ_(i) and length Σ_(S=1) ^(Si) length_(i,s), then the weighted anglespan is defined as:

$\begin{matrix}{\psi = \frac{\max \left\{ {{{{V_{i} \times V_{j}}}i},{j \in I}} \right\}}{\max \left\{ {{V_{i}}^{2}{i \in I}} \right\}}} & (10)\end{matrix}$

In short, the weighted angle span is the largest normalized area definedby two aggregated road vectors (block 706).

There are several reasons for adding this factor. First, the spatialdistribution of the road vector data is not uniform. For certain ruralareas, very few or no road vector may exist, and the algorithm will haveless data to aggregate for road signal detection, especially when thetile size used in subdividing the raster data is small. Second is thatwhen all road vectors inside a tile have about the same direction, it ismore like the last degenerated case discussed in the Special Casessection above. The translation precision will be more vulnerable tonoises and local errors because many optimal solutions may exist for theminimum-square match, even though the road detection may still workperfectly. For such cases, the corresponding angle span will berelatively small, and can help to indicate a possible problem.

Approximation Interpolation and QA Using Confidence Ratio

In an embodiment, tiles with low-confidence ratios may be inspected(block 416 of FIG. 4). For applications where very precise alignment isnot a critical requirement low-confidence tiles may not be manuallyinspected. With the help of the confidence ratio, a global warp methodcan generate better alignment results by assigning control point weightproportional to its confidence ratio, and computing an approximationwarp instead of an interpolation at places of low confidence ratio.Thus, an automatic registration pipeline can be formed without humaninteraction.

For applications where very precise alignment is a critical requirement,the automatic alignment results can be sorted and filtered to find lowreliability locations (block 418). Then the automatic generatedtranslation at these locations can be manually examined and replacedwith a correct translation when necessary (block 420).

For example, FIG. 14 shows the computed confidence ratio for 89 tilesfrom an example dataset. Those tiles with a confidence ratio of 1.0 areresults of empty image or vector data on the boundary of the image,which are safe to be ignored. To inspect the quality, only a few caseswith relatively low confidence ratios need to be checked. FIG. 15depicts three representations of the tile with the lowest confidenceratio. The image of this tile is depicted in 1501. In 1502, the vectordata is overlaid on the tile in blue and the detected streets areoverlaid in green and red. For this tile, the algorithm over-translatedin the correct direction because there are two parallel streets in thehorizontal vector search buffer but only one road vector in the roaddata, this is depicted in 1503. Because the lower road is longer andless obstructed, it outweighed the upper road. Upon manual examination,the translation vector for this tile may be replaced with the correcttranslation.

Automatic Alignment Warp Using Thin-Plate Spline Transform

From each tile, a pair of control points is generated using the computedtranslation vector for that tile (blocks 422 and 424 of FIG. 4). Thesource control point is chosen as the center pixel of the tile, and thecorresponding target control point is the source control point plus thetranslation vector for the tile. The control points from all tiles forma mesh. A thin-plate spline transformation is generated using thesecontrol points (block 426) and warps the raster data to more closelyalign with the road data resulting in the final aligned raster andvector data (block 428).

Example Computer System Implementation

Various aspects of the present invention can be implemented by software,firmware, hardware, or a combination thereof. FIG. 16 shows a rasterdata and vector data aligner 1600 according to an embodiment of thepresent invention. Raster data and vector data aligner 1600 includes avector direction detector 1602, edge filter 1604, roughness filter 1606,combiner 1608, feature width detector 1610 and translation generator1612. Raster data and vector data aligner 1600 can be implemented in bysoftware, firmware, hardware, or a combination thereof, it can be partof any geographical information system.

Vector direction detector 1602 can detect the direction of anapproximate vector of a feature within raster data. In one examplevector director detector 1602 can carry out each of the blocks 506 and508 described above. Edge filter 1604 can filter raster data in thedetected direction to produce an edge signal. In one example edge filter1604 can carry out each of the blocks 618, 620 and 622 described above.Roughness filter 1606 can filter raster data in the detected directionto produce a smoothness signal. In one example roughness filter 1606 cancarry out each of the blocks 610, 612, 614 and 616 described above.Combiner 1608 combines the edge signal and the smoothness signal into acombined signal. In one example combiner 1608 can carry out block 626described above. Feature width detector 1610 can filter the combinedsignal by a feature width before generating the translation information.In one example feature width detector 1610 can carry out block 628described above. Translation generator 1612 uses the combined signal togenerate translation information for the raster data. In one exampletranslation generator 1612 can carry out each of the blocks in flowdiagram 700 described above.

FIG. 17 illustrates an example computer system 1700 in which the presentinvention, or portions thereof, can be implemented as computer-readablecode. Various embodiments of the invention are described in terms ofthis example computer system 1700. After reading this description, itwill become apparent to a person skilled in the relevant art(s) how toimplement the invention using other computer systems and/or computerarchitectures.

Computer system 1700 includes one or more processors, such as processor1704. Processor 1704 can be a special purpose or a general purposeprocessor. Processor 1704 is connected to a communication infrastructure1706 (for example, a bus or network).

Computer system 1700 also includes a main memory 1708, and may alsoinclude a secondary memory 1710. Main memory 1708 may include, forexample, cache, and/or static and/or dynamic RAM. Secondary memory 1710may include, for example, a hard disk drive 1712 and/or a removablestorage drive 1714. Removable storage drive 1714 may comprise a floppydisk drive, a magnetic tape drive, an optical disk drive, a flashmemory, or the like. The removable storage drive 1714 reads from and/orwrites to a removable storage unit 1718 in a well known manner.Removable storage unit 1718 may comprise a floppy disk, magnetic tape,optical disk, flash memory, etc., which is read by and written to byremovable storage drive 1714. As will be appreciated by persons skilledin the relevant art(s), removable storage unit 1718 includes a computerusable storage medium having stored therein computer software and/ordata.

In alternative implementations, secondary memory 1710 may include othersimilar means for allowing computer programs or other instructions to beloaded into computer system 1700. Such means may include, for example, aremovable storage unit 1722 and an interface 1720. Examples of suchmeans may include a program cartridge and cartridge interface (such asthat found in video game devices), a removable memory chip (such as anEPROM, or PROM) and associated socket, and other removable storage units1722 and interfaces 1720 which allow software and data to be transferredfrom the removable storage unit 1722 to computer system 1700.

Computer system 1700 may also includes a main memory 1702. Main memory1702 may include, for example, cache, and/or static and/or dynamic RAM.Main memory 1702 may be separate from main memory 1708 or may be a partthereof. Main memory 1702 may be adapted to communicate with displayunit 1716. Display unit 1716 may comprise a computer monitor or similarmeans for displaying graphics, text, and other data received from mainmemory 1702.

Computer system 1700 may also include a communications interface 1724.Communications interface 1724 allows software and data to be transferredbetween computer system 1700 and external devices. Communicationsinterface 1724 may include a modem, a network interface (such as anEthernet card), a communications port, a PCMCIA slot and card, or thelike. Software and data transferred via communications interface 1724are in the form of a plurality of signals, hereinafter referred to assignals 1728, which may be electronic, electromagnetic, optical, orother signals capable of being received by communications interface1724. Signals 1728 are provided to communications interface 1724 via acommunications path 1726. Communications path 1726 carries signals 1728and may be implemented using wire or cable, fiber optics, a phone line,a cellular phone link, an RF link or other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage unit 1718, removable storage unit 1722, a hard disk installed inhard disk drive 1712, and signals 1728 carried over communications path1726. Computer program medium and computer usable medium can also referto memories, such as main memory 1708 and secondary memory 1710, whichcan be memory semiconductors (e.g. DRAMs, etc.). These computer programproducts are means for providing software to computer system 1700.

Computer programs (also called computer control logic) are stored inmain memory 1708 and/or secondary memory 1710. Computer programs mayalso be received via communications interface 1724. Such computerprograms, when executed, enable computer system 1700 to implement thepresent invention as discussed herein. In particular, the computerprograms, when executed, enable processor 1704 to implement theprocesses of the present invention, such as the steps in the methodillustrated by flowchart 400 of FIG. 4 discussed above. Accordingly,such computer programs represent controllers of the computer system1700. Where the invention is implemented using software, the softwaremay be stored in a computer program product and loaded into computersystem 1700 using removable storage drive 1714, interface 1720, harddrive 1712 or communications interface 1724.

Embodiments of the invention also may be directed to computer productscomprising software stored on any computer usable medium. Such software,when executed in one or more data processing device, causes a dataprocessing device(s) to operate as described herein. Embodiments of theinvention employ any computer usable or readable medium, known now or inthe future. Examples of computer usable mediums include, but are notlimited to, primary storage devices (e.g., any type of random accessmemory), secondary storage devices (e.g., hard drives, floppy disks, CDROMS, ZIP disks, tapes, magnetic storage devices, optical storagedevices, MEMS, nanotechnological storage device, etc.), andcommunication mediums (e.g., wired and wireless communications networks,local area networks, wide area networks, intranets, etc.).

Exemplary embodiments of the present invention have been presented. Theinvention is not limited to these examples. These examples are presentedherein for purposes of illustration, and not limitation. Alternatives(including equivalents, extensions, variations, deviations, etc., ofthose described herein) will be apparent to persons skilled in therelevant art(s) based on the teachings contained herein. Suchalternatives fall within the scope and spirit of the invention.

1. A method of aligning raster data and vector data, comprising;receiving raster data and a set of approximate vectors of vector dataassociated with features within the raster data; identifying directionsof the vectors and grouping the vectors into one or more directionalchunks based on their directions; defining one or more search areas ofthe raster data for each directional chunk, each area encompassing atleast one vector; generating translation information for each searcharea and corresponding at least one vector, said translation informationcomprising one or more of an offset vector and a signal weight; andgenerating one or more of a translation vector and a confidence ratiofrom the translation information of the one or more search areas.
 2. Themethod of claim 1, wherein defining the one or more search areascomprises defining a rectangle having a length of at least a length ofthe vector, and a predefined width.
 3. The method of claim 1, whereingenerating a translation vector comprises calculating the translationvector with a minimum-square best-match algorithm.
 4. The method ofclaim 3, wherein calculating the translation vector comprisescalculating the translation vector in accordance with:${{cost}\left( {r,\alpha} \right)} = {\sum\limits_{i = 1}^{I}{\sum\limits_{j = 1}^{J_{i}}{W_{i,j}\left( {L_{i,j} - {r\; {\cos \left( {\alpha - \theta_{i}} \right)}}} \right)}^{2}}}$wherein (r,α) represents the translation vector of length r anddirection α, cost(r,α) represents a target cost function, W_(i,j)represents a signal weight of the j^(th) vector of direction θ_(i), andL_(i,j) represents a location of the j^(th) vector of direction θ_(i).5. The method of claim 1, wherein generating a confidence ratiocomprises calculating a weighted angle span and a normalized distancefactor for the raster data and dividing the weighted angle span by thenormalized distance factor.
 6. The method of claim 5, whereincalculating the weighted angle span comprises calculating the weightedangle span in accordance with:$\psi = \frac{\max \left\{ {{{{V_{i} \times V_{j}}}i},{j \in I}} \right\}}{\max \left\{ {{V_{i}}^{2}{i \in I}} \right\}}$wherein ψ represents the weighted angle span, V_(i) represents anaggregated road vector of direction θ_(i), V_(j) represents anaggregated road vector of direction θ_(j).
 7. The method of claim 5,wherein calculating the normalized distance factor comprises calculatingthe normalized distance factor in accordance with:$\omega = \sqrt{\frac{{cost}\left( {r_{0},\alpha_{0}} \right)}{\sum\limits_{i,j}W_{i,j}}}$wherein ω represents the normalized distance factor, cost(r₀,α₀)represents the value of the target cost function for r equal to r₀ and aequal to α₀.
 8. The method of claim 1, wherein the raster data includesa satellite image.
 9. The method of claim 8, wherein the set ofapproximate vectors of vector data includes approximate street vectorsthat approximate locations of streets present in the satellite image.10. The method of claim 9, further comprising aligning the approximatestreet vectors with respective vectors that better approximate thelocations of the streets present in the satellite image.
 11. Anon-transitory computer readable storage medium having instructionsstored thereon that, when executed by a processor, cause the processorto perform a method, the method comprising: receiving raster data and aset of approximate vectors of vector data associated with featureswithin the raster data; identifying directions of the vectors andgrouping the vectors into one or more directional chunks based on theirdirections; defining one or more search areas of the raster data foreach directional chunk, each area encompassing at least one vector;generating translation information for each search area andcorresponding at least one vector, said translation informationcomprising one or more of an offset vector and a signal weight; andgenerating one or more of a translation vector and a confidence ratiofrom the translation information of the one or more search areas. 12.The computer readable storage medium of claim 10, wherein defining theone or more search areas comprises defining a rectangle having a lengthof at least a length of the vector, and a predefined width.
 13. Thecomputer readable storage medium of claim 10, wherein generating atranslation vector comprises calculating the translation vector with aminimum-square best-match algorithm.
 14. The computer readable storagemedium of claim 13, wherein calculating the translation vector comprisescalculating the translation vector in accordance with:${{cost}\left( {r,\alpha} \right)} = {\sum\limits_{i = 1}^{I}{\sum\limits_{j = 1}^{J_{i}}{W_{i,j}\left( {L_{i,j} - {r\; {\cos \left( {\alpha - \theta_{i}} \right)}}} \right)}^{2}}}$wherein (r,α) represents the translation vector of length r anddirection α, cost(r,α) represents a target cost function, W_(i,j)represents a signal weight of the j^(th) vector of direction θ_(i), andL_(i,j) represents a location of the j^(th) vector of direction θ_(i).15. The computer readable storage medium of claim 10, wherein generatinga confidence ratio comprises calculating a weighted angle span and anormalized distance factor for the raster data and dividing the weightedangle span by the normalized distance factor.
 16. The computer readablestorage medium of claim 15, wherein calculating the weighted angle spancomprises calculating the weighted angle span in accordance with:$\psi = \frac{\max \left\{ {{{{V_{i} \times V_{j}}}i},{j \in I}} \right\}}{\max \left\{ {{V_{i}}^{2}{i \in I}} \right\}}$wherein ψ represents the weighted angle span, V_(i) represents anaggregated road vector of direction θ_(i), V_(j) represents anaggregated road vector of direction θ_(j).
 17. The computer readablestorage medium of claim 15, wherein calculating the normalized distancefactor comprises calculating the normalized distance factor inaccordance with:$\omega = \sqrt{\frac{{cost}\left( {r_{0},\alpha_{0}} \right)}{\sum\limits_{i,j}W_{i,j}}}$wherein ω represents the normalized distance factor, cost(r₀,α₀)represents the value of the target cost function for r equal to r₀ and αequal to α₀.
 18. The computer readable storage medium of claim 11,wherein the raster data includes a satellite image.
 19. The computerreadable storage medium of claim 18, wherein the set of approximatevectors of vector data includes approximate street vectors thatapproximate locations of streets present in the satellite image.
 20. Thecomputer readable storage medium of claim 19, wherein the method furthercomprises aligning the approximate street vectors with respectivevectors that better approximate the locations of the streets present inthe satellite image.